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--- |
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license: apache-2.0 |
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base_model: bert-large-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: bert-large-uncased-Fake_Reviews_Classifier |
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results: [] |
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--- |
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# bert-large-uncased-Fake_Reviews_Classifier |
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This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5336 |
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- Accuracy: 0.8381 |
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- F1 |
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- Weighted: 0.8142 |
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- Micro: 0.8381 |
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- Macro: 0.6308 |
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- Recall |
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- Weighted: 0.8381 |
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- Micro: 0.8381 |
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- Macro: 0.6090 |
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- Precision |
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- Weighted: 0.8101 |
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- Micro: 0.8381 |
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- Macro: 0.7029 |
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## Model description |
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For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Binary%20Classification/Fake%20Reviews/Fake%20Reviews%20Classification%20-%20BERT-Large%20With%20PEFT.ipynb |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. You are welcome to test and experiment with this model, but it is at your own risk/peril. |
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## Training and evaluation data |
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Dataset Source: https://www.kaggle.com/datasets/razamukhtar007/fake-reviews |
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__Histogram of Word Counts of Reviews__ |
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__Class Distribution__ |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted F1 | Micro F1 | Macro F1 | Weighted Recall | Micro Recall | Macro Recall | Weighted Precision | Micro Precision | Macro Precision | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| |
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| 0.633 | 1.0 | 10438 | 0.5608 | 0.8261 | 0.7914 | 0.8261 | __0.5745__ | 0.8261 | 0.8261 | 0.5643 | 0.7844 | 0.8261 | 0.6542 | |
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| 0.6029 | 2.0 | 20876 | 0.6490 | 0.8331 | 0.7724 | 0.8331 | __0.5060__ | 0.8331 | 0.8331 | 0.5239 | 0.7892 | 0.8331 | 0.6929 | |
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| 0.5478 | 3.0 | 31314 | 0.5508 | 0.8305 | 0.8071 | 0.8305 | __0.6189__ | 0.8305 | 0.8305 | 0.6003 | 0.8002 | 0.8305 | 0.6784 | |
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| 0.513 | 4.0 | 41752 | 0.5459 | 0.8347 | 0.8101 | 0.8347 | __0.6224__ | 0.8347 | 0.8347 | 0.6023 | 0.8049 | 0.8347 | 0.6916 | |
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| 0.5288 | 5.0 | 52190 | 0.5336 | 0.8381 | 0.8142 | 0.8381 | __0.6308__ | 0.8381 | 0.8381 | 0.6090 | 0.8101 | 0.8381 | 0.7029 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
